mle-workflow

The saved excerpt is a snapshot from when this skill was listed. The external source remains the complete and most current version.

---
name: mle-workflow
description: Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
license: MIT
metadata:
  origin: ECC
---

# Machine Learning Engineering Workflow

Use this skill to turn model work into a production ML system with clear data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

## When to Activate

- Planning or reviewing a production ML feature, model refresh, ranking system, recommender, classifier, embedding workflow, or forecasting pipeline
- Converting notebook code into a reusable training, evaluation, batch inference, or online inference pipeline
- Designing model promotion criteria, offline/online evals, experiment tracking, or rollback paths
- Debugging failures caused by data drift, label leakage, stale features, artifact mismatch, or inconsistent training and serving logic
- Adding model monitoring, canary rollout, shadow traffic, or post-deploy quality checks

## Scope Calibration

Use only the lanes that fit the system in front of you. This skill is useful for ranking, search, recommendations, classifiers, forecasting, embeddings, LLM workflows, anomaly detection, and batch analytics, but it should not force one architecture onto all of them.

- Do not assume every model has supervised labels, online serving, a feature store, PyTorch, GPUs, human review, A/B tests, or real-time feedback.
- Do not add heavyweight MLOps machinery when a data contract, baseline, eval script, and rollback note would make the change reviewable.
- Do make assumptions explicit when the project lacks labels, delayed outcomes, slice definitions, production traffic, or monitoring ownership.
- Treat examples as interchangeable scaffolds. Replace metrics, serving mode, data stores, and rollout mechanics with the project-native equivalents.

## Related Skills

- `python-patterns` and `python-testing` for Python implementation and pytest coverage
- `pytorch-patterns` for deep learning models, data loaders, device handling, and training loops
- `eval-harness` and `ai-regression-testing` for promotion gates and agent-assisted regression checks
- `database-migrations`, `postgres-patterns`, and `clickhouse-io` for data storage and analytics surfaces
- `deployment-patterns`, `docker-patterns`, and `security-review` for serving, secrets, containers, and production hardening

## Reuse the SWE Surface

Do not treat MLE as separate from software engineering. Most ECC SWE workflows apply directly to ML systems, often with stricter failure modes:

The recommended `minimal --with capability:machine-learning` install keeps the core agent surface available alongside this skill. For skill-only or agent-limited harnesses, pair `skill:mle-workflow` with `agent:mle-reviewer` where the target supports agents.

| SWE surface | MLE use |
|-------------|---------|
| `product-capability` / `architecture-decision-records` | Turn model work into explicit product contracts and record irreversible data, model, and rollout choices |
| `repo-scan` / `codebase-onboarding` / `code-tour` | Find existing training, feature, serving, eval, and monitoring paths before introducing a parallel ML stack |
| `plan` / `feature-dev` | Scope model changes as product capabilities with data, eval, serving, and rollback phases |
| `tdd-workflow` / `python-testing` | Test feature transforms, split logic, metric calculations, artifact loading, and inference schemas before implementation |
| `code-reviewer` / `mle-reviewer` | Review code quality plus ML-specific leakage, reproducibility, promotion, and monitoring risks |
| `build-fix` / `pr-test-analyzer` | Diagnose broken CI, flaky evals, missing fixtures, and environment-specific model or dependency failures |
| `quality-gate` / `test-coverage` | Require automated evidence for transforms, me